提出新算法，让未知因素的图模型也能高效推理。
Lifting Factor Graphs with Some Unknown Factors for New Individuals
- 通过识别不可区分子图，实现已知势能向未知势能转移
- 在含未知因素图中保持精确推理，提升效率
- 适合处理个体信息不全的复杂概率建模问题
提升方法通过使用不可区分对象的代表元来利用概率图模型中的对称性，从而更高效地进行查询回答且保持答案精确。本文研究如何使提升机制适用于包含未知因素的因子图，即其潜在映射函数未知的因子。我们提出LIFAGU算法，用于识别含未知因子的因子图中不可区分的子图，从而将已知势能转移到未知势能上，确保模型语义明确并支持（提升式）概率推断。进一步地，我们将背景知识——某些因子属于同一对象个体——融入算法，以减少已知势能向未知势能转移时的歧义性。
原文摘要 · Abstract (English)
Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers. In this paper, we investigate how lifting enables us to perform probabilistic inference for factor graphs containing unknown factors, i.e., factors whose underlying function of potential mappings is unknown. We present the Lifting Factor Graphs with Some Unknown Factors (LIFAGU) algorithm to identify indistinguishable subgraphs in a factor graph containing unknown factors, thereby enabling the transfer of known potentials to unknown potentials to ensure a well-defined semantics of the model and allow for (lifted) probabilistic inference. We further extend LIFAGU to incorporate additional background knowledge about groups of factors belonging to the same individual object. By incorporating such background knowledge, LIFAGU is able to further reduce the ambiguity of possible transfers of known potentials to unknown potentials.
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